Data Governance – How is it different from Data Engineering & Data Analysis?

Introduction

According to a report by IDC, there will be over 175 Zettabytes of data worldwide by 2025 – a whopping surge of 5 times from the count in 2018. Similarly, the Big Data analytics market is also expected to reach over USD 745 Billion by 2030 with a CAGR of 13.5% during the period, 2023-2030.

The figures above prove that we are leading towards a data-driven era where businesses rely on data to form better decisions. Data is helping companies with market insights, customer behaviour, competitor analysis and much more to grow their business. However, data alone is of no use, unless it is treated with proper data governance and strategy to derive meaningful information.

This blog comprehensively covers the basics of data governance, its components and its importance. Moreover, the blog also covers how data governance differs from data engineering and data analysis to help you get started in the field of data engineering in 2023.

What is Data Governance – Components and Benefits

Data Governance refers to a framework for managing, processing and securing data in an enterprise. It ensures the availability, reliability and consistency of the data for the business purpose. With the help of effective strategies and policies, a data governance scheme states what data is to be stored, where the data is to be stored, who can access the data, how the data will be processed and much more to establish data-driven decision-making.

The major components of Data Governance are –

•   Process
•   Strategy
•   Peoples
•   Technology
•   Policies
•   Standards
•   Metrics/KPIs
•   Security

A well-crafted data governance framework not just ensures data security and visibility, but strengthens data quality helping organizations in deriving enhanced insights. In fact, according to a report, businesses with a designated data governance or data management leader show 42% greater confidence in data quality than those without.

Importance of Data Governance

Organizations today are witnessing an immense amount of data like never before, especially the medium and large companies which deal with 'Big Data'. Therefore, it becomes important to have a robust data governance scheme. Some of its main importance are –

1. It lays the foundation for a data-driven organization
2. It helps in integrating data across different departments
3. It helps in mapping data and increases visibility
4. It helps in compliance with regulatory authorities
5. It ensures data consistency and prevents mishandling

Data Governance vs Data Engineering vs Data Analysis – Complete Comparison

Many a time, people confuse data governance with data engineering and data analysis. Even though closely related, they are far different from each other.

Data Engineering

Data Engineering refers to building and maintaining data pipelines and infrastructure for further data analysis. It is the process of collecting data from different sources and validating the data quality with standards based on the governance framework. A data engineer is responsible for carrying out the process of data engineering.

A data engineer needs to have a strong understanding of software engineering and data architectures and models. Additionally, one must be adept at Kafka, Hadoop, Spark, AWS and SQL.

Data Analysis

Data Analysis is the process of cleaning and refining data to draw inferences and useful insights for business decision-making. An analytics engineer or a data analyst is responsible for data analysis.

An analytics engineer needs to have a strong analytical mindset and the ability to define data outcomes intuitively. Some of the common tools are Python, Tableau, PowerBI, Excel and R-Language.

Data Governance

Data governance deals with the selection and management of data in an organisation. It ensures data quality and consistency for further analysis to derive meaningful information from these data. It encompasses various domains like data engineering, data analysis, analytics engineering and much more.

So, the basic difference between the three is that while data engineering and data analysis are individual domains, data governance is a holistic field comprising various sub-domains.

Therefore, a person planning to make a career in data governance must have solid command over data engineering along with sound knowledge of skills like data analysis, data architecture, data modelling, database management, project management and much more.

Conclusion

According to a report, the global data governance market size is approximately USD 2.73 billion in 2023 and is expected to reach over USD 6.71 billion by 2028 with a CAGR of 19.72% over the forecast period (2023-2028). This signifies the rising impact of data governance and management in organizations, which is not limited to but includes better decision-making, data security and data consistency.

However, it should be noted that data governance is a broad field and requires knowledge of various sub-domains like data engineering and data analysis.